Other-race faces : limitations of expert face recognition /

Adults' expert face recognition is limited to the kinds of faces they encounter on a daily basis (typically upright human faces of the same race). Adults process own-race faces holistically (Le., as a gestalt) and are exquisitely sensitive to small differences among faces in the spacing of features, the shape of individual features and the outline or contour of the face (Maurer, Le Grand, & Mondloch, 2002), however this expertise does not seem to extend to faces from other races. The goal of the current study was to investigate the extent to which the mechanisms that underlie expert face processing of own-race faces extend to other-race faces. Participants from rural Pennsylvania that had minimal exposure to other-race faces were tested on a battery of tasks. They were tested on a memory task, two measures of holistic processing (the composite task and the part/whole task), two measures of spatial and featural processing (the JanelLing task and the scrambledlblurred faces task) and a test of contour processing (JanelLing task) for both own-and other-race faces. No study to date has tested the same participants on all of these tasks. Participants had minimal experience with other-race faces; they had no Chinese family members, friends or had ever traveled to an Asian country. Results from the memory task did not reveal an other-race effect. In the present study, participants also demonstrated holistic processing of both own- and other-race faces on both the composite task and the part/whole task. These findings contradict previous findings that Caucasian adults process own-race faces more holistically than other-race faces. However participants did demonstrate an own-race advantage for processing the spacing among features, consistent with two recent studies that used different manipulations of spacing cues (Hayward et al. 2007; Rhodes et al. 2006). They also demonstrated an other-race effect for the processing of individual features for the Jane/Ling task (a direct measure of featural processing) consistent with previous findings (Rhodes, Hayward, & Winkler, 2006), but not for the scrambled faces task (an indirect measure offeatural processing). There was no own-race advantage for contour processing. Thus, these results lead to the conclusion that individuals may show less sensitivity to the appearance of individual features and the spacing among them in other-race faces, despite processing other-race faces holistically.

An analysis of the photoelectron and Rydberg states of formaldehyde /|nby Carol R. Lessard. -- 260 St. Catharines [Ont.] : Dept. of Chemistry, Brock University,

Although it is generally accepted that Rydberg orbitals are very large and diffuse, and that electron promotion to a Rydberg orbital is not too different from ionization of the molecule, analysis of the two types of transitions proves otherwise. The photoelectron spectrum of the 2B2 (n) ion has very little vibrational structure attached to the origin band; on the other hand, several of the Rydberg transitions which involve the promotion of the n(bZ) electron exhibit a great deal of vibrational activity. In particular, the members of the n=3 Rydberg\ series interact with and perturb each other through pseudo-Jahn-Teller vibronic coupling. The vacuum ultraviolet spectrum contains a number of features which are difficult to explain, and two unusually sharp bands can only be identified as representing some form of electron promotion in formaldehyde.

Economic security & reconstruction: utility, history and practice in post-conflict environments

Please consult the paper edition of this thesis to read. It is available on the 5th Floor of the Library at Call Number: Z 9999 P65 D53 2007

Feature Selection and Classification Using Age Layered Population Structure Genetic Programming

The curse of dimensionality is a major problem in the fields of machine learning, data mining and knowledge discovery. Exhaustive search for the most optimal subset of relevant features from a high dimensional dataset is NP hard. Sub–optimal population based stochastic algorithms such as GP and GA are good choices for searching through large search spaces, and are usually more feasible than exhaustive and deterministic search algorithms. On the other hand, population based stochastic algorithms often suffer from premature convergence on mediocre sub–optimal solutions. The Age Layered Population Structure (ALPS) is a novel metaheuristic for overcoming the problem of premature convergence in evolutionary algorithms, and for improving search in the fitness landscape. The ALPS paradigm uses an age–measure to control breeding and competition between individuals in the population. This thesis uses a modification of the ALPS GP strategy called Feature Selection ALPS (FSALPS) for feature subset selection and classification of varied supervised learning tasks. FSALPS uses a novel frequency count system to rank features in the GP population based on evolved feature frequencies. The ranked features are translated into probabilities, which are used to control evolutionary processes such as terminal–symbol selection for the construction of GP trees/sub-trees. The FSALPS metaheuristic continuously refines the feature subset selection process whiles simultaneously evolving efficient classifiers through a non–converging evolutionary process that favors selection of features with high discrimination of class labels. We investigated and compared the performance of canonical GP, ALPS and FSALPS on high–dimensional benchmark classification datasets, including a hyperspectral image. Using Tukey’s HSD ANOVA test at a 95% confidence interval, ALPS and FSALPS dominated canonical GP in evolving smaller but efficient trees with less bloat expressions. FSALPS significantly outperformed canonical GP and ALPS and some reported feature selection strategies in related literature on dimensionality reduction.

Feature Selection and Classification Using Age Layered Population Structure Genetic Programming

The curse of dimensionality is a major problem in the fields of machine learning, data mining and knowledge discovery. Exhaustive search for the most optimal subset of relevant features from a high dimensional dataset is NP hard. Sub–optimal population based stochastic algorithms such as GP and GA are good choices for searching through large search spaces, and are usually more feasible than exhaustive and determinis- tic search algorithms. On the other hand, population based stochastic algorithms often suffer from premature convergence on mediocre sub–optimal solutions. The Age Layered Population Structure (ALPS) is a novel meta–heuristic for overcoming the problem of premature convergence in evolutionary algorithms, and for improving search in the fitness landscape. The ALPS paradigm uses an age–measure to control breeding and competition between individuals in the population. This thesis uses a modification of the ALPS GP strategy called Feature Selection ALPS (FSALPS) for feature subset selection and classification of varied supervised learning tasks. FSALPS uses a novel frequency count system to rank features in the GP population based on evolved feature frequencies. The ranked features are translated into probabilities, which are used to control evolutionary processes such as terminal–symbol selection for the construction of GP trees/sub-trees. The FSALPS meta–heuristic continuously refines the feature subset selection process whiles simultaneously evolving efficient classifiers through a non–converging evolutionary process that favors selection of features with high discrimination of class labels. We investigated and compared the performance of canonical GP, ALPS and FSALPS on high–dimensional benchmark classification datasets, including a hyperspectral image. Using Tukey’s HSD ANOVA test at a 95% confidence interval, ALPS and FSALPS dominated canonical GP in evolving smaller but efficient trees with less bloat expressions. FSALPS significantly outperformed canonical GP and ALPS and some reported feature selection strategies in related literature on dimensionality reduction.